基于MCR-ALS高光谱重构的鲑鱼脂肪可视化研究  

Salmon Fat Visualization Based on MCR-ALS Hyperspectral Reconstruction

作　　者：章海亮 谢潮勇 罗微 王琛 聂训 田彭 刘雪梅[3] 詹白勺 ZHANG Hai-liang;XIE Chao-yong;LUO Wei;WANG Chen;NIE Xun;TIAN Peng;LIU Xue-mei;ZHAN Bai-shao(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Lunan Technician College,Linyi 276000,China;School of Civil Engineering and Architecture,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学电气与自动化工程学院,江西南昌330013 [2]鲁南技师学院,山东临沂276000 [3]华东交通大学土木建筑学院,江西南昌330013

出　　处：《光谱学与光谱分析》2023年第8期2601-2607,共7页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41867020,32260622,62265007);江西省03专项及5G项目(20212ABC03A17);江西省自然科学基金项目(20224BAB212007)资助。

摘　　要：人们对鱼类的品质追求越来越高,因此对于开发水产养殖中鱼类重要参数脂肪含量的检测显得愈发的重要,传统的检测方法虽然经过许多研究人员的修改和改进,但仍然存在费时费力,需要专业的人员培训存在一些问题。光谱技术也存在仅使用整条鱼片作为预测样本,缺乏普遍性,整个鱼片的成分分布不均匀,采样时间过长等导致图像质量不高等问题,该研究通过MCR-ALS算法重建后的数据和图像的增益效果,评估了采用近红外高光谱成像技术预测并实现鲑鱼片重要参数(脂肪)可视化的可行性。首先将购买的新鲜三文鱼按照背面和腹部切块分割,每条三文鱼制作成20个样本,共100个样本,其中75个样本用于校正集,25个样本用于预测集。用高光谱成像系统采集三文鱼样本的光谱数据,再通过索氏提取器测定三文鱼脂肪的含量,并建立其理化值样本,然后通过MCR-ALS对光谱数据进行重构,发现重构后的光谱有效信息随着组分推荐评分上升,通过连续投影算法(SPA)选择特征波长,并建立最小二乘支持向量机(LS-SVM)模型评估两种预测效果(原始和重建数据)。MCR-ALS-SPA-LS-SVM的预测精度最高,R_(p)=0.9555,RMESP=1.6505,RPD=3.3899;采用MCR-ALS和未处理的模型对鱼片脂肪进行视觉图像预测,大大减少了噪音的输入,有效还原了鱼片的轮廓,并且令鱼的脂肪条纹更加的清晰,图像质量更优。进一步分析聚类图像,通过不同成分的主成分贡献和相同成分的主成分贡献比,发现类别为20种时,样品与背景簇存在干扰,然而采用少量的簇类分析发现,仅5和10个种类即可完整描绘出整个样品的轮廓,对于光谱强反应物质存在很好的聚类效果,具有简化模型的可能。无论是数据还是图像,令人满意的预测结果证实了近红外高光谱成像用于鲑鱼脂肪定量和视觉图像预测的可行性,并且算法的优化大大缩短了检测时间,为实时�People s pursuit of fish quality is getting higher and higher,so it is more and more important to develop the detection of the fat content of important parameters of fish in aquaculture.Although many researchers have modified and improved traditional detection methods,they are still time-consuming.It is laborious and requires professional personnel training.There are some problems.The emerging spectral technology also has problems such as low image quality caused by only using the whole fish fillet as a prediction sample,lack of universality,uneven distribution of components of the whole fish fillet,and long sampling time.This study uses the MCR-ALS algorithm.After reconstruction of the data and image gain,the feasibility of predicting and visualizing an important parameter(fat)of salmon fillets using near-infrared hyperspectral imaging was assessed.First,the fresh salmon bought from the market is cut into pieces according to the back and abdomen.Each salmon is made into 20 samples,a total of 100 samples,of which 75 samples are used for the calibration set,and 25 samples are used for the prediction set.Then,the spectral data of salmon fish samples were collected by hyperspectral imaging system,the content of salmon fat was measured by Soxhlet extractor,and the physical and chemical value samples were established.Then the spectral data was reconstructed by MCR-ALS.It was found that the reconstructed spectrally valid information increases with the component recommendation score,and then the characteristic wavelengths are selected by a continuous projection algorithm(SPA),and a least squares support vector machine(LS-SVM)model is established to evaluate the two prediction effects(raw and reconstructed data).MCR-ALS-SPA-LS-SVM has the highest prediction accuracy,R_(p)=0.9555,RMESP=1.6505,RPD=3.3899.Then,using MCR-ALS and the unprocessed model to perform visual image prediction on fish fillet fat,its effect It greatly reduces the input of noise,effectively restores the outline of the fish fillet,and makes the fat stri

关 键 词：多元曲线分辨-交替最小二乘 鲑鱼 可视化 高光谱 脂肪 图像分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]